96 citations · 186 across the 9 of their papers we have counts for
10 papers
Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls
Saroj Gopali, Bipin Chhetri, Deepika Giri +2
Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT…
In-Context and Few-Shots Learning for Forecasting Time Series Data based on Large Language Models
Saroj Gopali, Bipin Chhetri, Deepika Giri +2
Existing data-driven approaches in modeling and predicting time series data include ARIMA (Autoregressive Integrated Moving Average), Transformer-based models, LSTM (Long Short-Ter…
The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data
Saroj Gopali, Sima Siami-Namini, Faranak Abri +1
As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not…
The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models
Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini +1
Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human i…
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models
Saroj Gopali, Faranak Abri, Sima Siami-Namini +1
There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning t…
A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic
Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli +1
It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic. The 2006 Great Recession a…
Clustering Time Series Data through Autoencoder-based Deep Learning Models
Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah +2
Machine learning and in particular deep learning algorithms are the emerging approaches to data analysis. These techniques have transformed traditional data mining-based analysis r…
The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities
Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah +2
The detection of zero-day attacks and vulnerabilities is a challenging problem. It is of utmost importance for network administrators to identify them with high accuracy. The highe…
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM
Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate r…
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM
Sima Siami-Namini, Akbar Siami Namin
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time s…